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Making the South African TIMES (SATIM)
Energy System Model ‘Water Smart’
Adrian Stone, Bruno Merven & James Cullis (Aurecon)
UN-Water Annual International Zaragoza Conference
Zaragoza, Spain – 13th January 2014
Research Groups in ERC
Energy and
climate
change

Energy
efficiency

ERC
Energy
Systems
Analysis

Energy,
poverty and
development
South Africa

at a glance…

~52 million people.
GDP ~6000 USD per capita (2011).
~1.2 million m2 (12%) of total land cover suitable for
crop production.
~60% of allocated water consumed for irrigated
agriculture.
~90% of population with access to electricity.
Increasing urbanisation of the population.
Increasing demand for housing and water and
sanitation services.
Eastern
Cape
8%

Highincome,
22%

Gauteng
34%

JHB

Lowincome,
42%

Population distribution by income

Other, 5.5
0%

Industry, 3
%
Municipalrural, 3%

DBN
Midincome,
36%

Mining, 2. Electricity,
2%
50%

KwaZuluNatal
16%

CPT
Western
Cape
14%

Municipalurban, 24
%

Irrigation,
60%

Provincial share of GDP
Estimate of sectorial water use
National Water Resource Strategy

4
Water Marginal Cost Curves

Reconciliation of future demand and potential augmentation options for the Lephalale WMA
5
Water Supply Cost Curves

Provisional costs for future water supply augmentation in the Lephalale catchment
6
Water Supply Cost Curves under
Climate Change Risk

Potential impact of climate change on cost of future water supply options in the Lephalale WMA.
7
National Energy Model – Key Points
• South African TIMES Model (SATIM)
• Partial equilibrium linear least-cost optimisation model
capable of representing the whole energy
system, including its economic costs and its emissions.
• A number of years of development - 2003 IEP, 2007 LTMS
• Sectoral Representation - Electricity & Transport sector
represented in most detail.
• Methodology & Assumptions in the public domain •
•
•

http://www.erc.uct.ac.za/Research/Otherdocs/Satim/SATIM%20Methodologyv2.1.pdf
http://www.erc.uct.ac.za/Research/esystems-group-satim.htm
http://www.erc.uct.ac.za/Research/publications/12-Mervenetal_Quantifying_energy_needs_transport%20sector.pdf
8
Main Features
• Bottom-up Energy Systems Least-Cost Optimization Long-Range
(>10 years) Planning Model (similar to the one used for the IEP)
• Full Sector: Includes and allows trade-off between demand and
Supply
• End-use type model:
– Gives a detailed description of how the energy is used.
– Describes the types of equipment used and how much energy is used by
each type of equipment to satisfy demand.
– Can be used to forecast useful energy as well as final energy demand
– Can capture:
•
•
•
•
•

structural changes/ shocks
mode switching (transport)
fuel switching
Technical improvement/ improved efficiency
Intensity changes e.g. mines have to dig deeper

Objective – Minimise the cost of supplying an energy service
TIMES – represent & cost entire energy system – cost
optimal pathways under constraints

10
Energy model components
• Made up of 2 simple components:
– Energy Carriers (e.g. fuels, demand)
– Technologies (e.g. Light bulb, power plant) all
characterized in the same way:
– Input and Output Carrier (Commodity)
These are the
– Efficiency
parameters
– Investment Costs per unit of capacity
that affect the
– Activity Costs
– Existing Capacity
cost of
– Annual Availability
supplying the
– Expected Life
– Emissions
energy service
Simple Reference Energy System

Commodities are input to and output from technologies along competing chains to
supply an energy service. Water can be one of these commodities if we know enough
about supply.
Scenarios we will look at…..
• Optimisation results with and without water
costs.
• Climate change impacts on water supply cost
curves
• 4 GHG constrained scenarios – contrasting 275
Mton cap on the power sector with CO2 tax
options based on National Treasury’s
proposed tax structure.
Shortcomings of TIMES
• No demand response (unless used with elastic
demand, in which case a price elasticity is
needed for each end-use)
• Impacts on the rest of the economy, and
socio-economic indicators not quantified
• For answering broad techno-economic
planning questions – neither a lot of
engineering or economic detail. Can be reliant
on good micro-analysis.

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Making the South African TIMES (SATIM) Energy System Model ‘Water Smart’, by Adrian Stone from the University of Cape Town

  • 1. Making the South African TIMES (SATIM) Energy System Model ‘Water Smart’ Adrian Stone, Bruno Merven & James Cullis (Aurecon) UN-Water Annual International Zaragoza Conference Zaragoza, Spain – 13th January 2014
  • 2. Research Groups in ERC Energy and climate change Energy efficiency ERC Energy Systems Analysis Energy, poverty and development
  • 3. South Africa at a glance… ~52 million people. GDP ~6000 USD per capita (2011). ~1.2 million m2 (12%) of total land cover suitable for crop production. ~60% of allocated water consumed for irrigated agriculture. ~90% of population with access to electricity. Increasing urbanisation of the population. Increasing demand for housing and water and sanitation services. Eastern Cape 8% Highincome, 22% Gauteng 34% JHB Lowincome, 42% Population distribution by income Other, 5.5 0% Industry, 3 % Municipalrural, 3% DBN Midincome, 36% Mining, 2. Electricity, 2% 50% KwaZuluNatal 16% CPT Western Cape 14% Municipalurban, 24 % Irrigation, 60% Provincial share of GDP Estimate of sectorial water use
  • 5. Water Marginal Cost Curves Reconciliation of future demand and potential augmentation options for the Lephalale WMA 5
  • 6. Water Supply Cost Curves Provisional costs for future water supply augmentation in the Lephalale catchment 6
  • 7. Water Supply Cost Curves under Climate Change Risk Potential impact of climate change on cost of future water supply options in the Lephalale WMA. 7
  • 8. National Energy Model – Key Points • South African TIMES Model (SATIM) • Partial equilibrium linear least-cost optimisation model capable of representing the whole energy system, including its economic costs and its emissions. • A number of years of development - 2003 IEP, 2007 LTMS • Sectoral Representation - Electricity & Transport sector represented in most detail. • Methodology & Assumptions in the public domain • • • http://www.erc.uct.ac.za/Research/Otherdocs/Satim/SATIM%20Methodologyv2.1.pdf http://www.erc.uct.ac.za/Research/esystems-group-satim.htm http://www.erc.uct.ac.za/Research/publications/12-Mervenetal_Quantifying_energy_needs_transport%20sector.pdf 8
  • 9. Main Features • Bottom-up Energy Systems Least-Cost Optimization Long-Range (>10 years) Planning Model (similar to the one used for the IEP) • Full Sector: Includes and allows trade-off between demand and Supply • End-use type model: – Gives a detailed description of how the energy is used. – Describes the types of equipment used and how much energy is used by each type of equipment to satisfy demand. – Can be used to forecast useful energy as well as final energy demand – Can capture: • • • • • structural changes/ shocks mode switching (transport) fuel switching Technical improvement/ improved efficiency Intensity changes e.g. mines have to dig deeper Objective – Minimise the cost of supplying an energy service
  • 10. TIMES – represent & cost entire energy system – cost optimal pathways under constraints 10
  • 11. Energy model components • Made up of 2 simple components: – Energy Carriers (e.g. fuels, demand) – Technologies (e.g. Light bulb, power plant) all characterized in the same way: – Input and Output Carrier (Commodity) These are the – Efficiency parameters – Investment Costs per unit of capacity that affect the – Activity Costs – Existing Capacity cost of – Annual Availability supplying the – Expected Life – Emissions energy service
  • 12. Simple Reference Energy System Commodities are input to and output from technologies along competing chains to supply an energy service. Water can be one of these commodities if we know enough about supply.
  • 13. Scenarios we will look at….. • Optimisation results with and without water costs. • Climate change impacts on water supply cost curves • 4 GHG constrained scenarios – contrasting 275 Mton cap on the power sector with CO2 tax options based on National Treasury’s proposed tax structure.
  • 14. Shortcomings of TIMES • No demand response (unless used with elastic demand, in which case a price elasticity is needed for each end-use) • Impacts on the rest of the economy, and socio-economic indicators not quantified • For answering broad techno-economic planning questions – neither a lot of engineering or economic detail. Can be reliant on good micro-analysis.

Hinweis der Redaktion

  1. Bureau of Market Research, Personal Income Patterns and Profiles for South Africa 2009, 2010. "R50 000-R300 000 (or emerging middle class)“Sources: StatsSA, DWAF and ERC.About a third of households live on an income less than R19,600 (2005 rands) per annum which is equivalent to US$3100 (2013 $) or us$8.55 per day. So much of the electrified population don’t consume much.CPT=Cape Town (provincial capital of the Western Cape); JHB = Johannesburg (provincial capital of Gauteng); DBN = Durban (provincial capital of Kwazulu-Natal)30th most water scarce country in the world. (http://www.dwaf.gov.za/nwrs/)
  2. Overview of the current and projected future water resources situation across the country. The bars represent the difference between demand and the available supply and the potential for development of the resource (e.g. through the construction of new schemes). Of particular importance is the transfer of water between WMAs as indicated by the arrows. The important conclusions that can be drawn from this is that water is not available evenly over the whole country, and that this will have potential impacts on the marginal cost of water for new plants constructed in different locations, particularly if additional inter-basin transfers are required
  3. WRYM determines the yields of dams (existing or proposed) in a complex system of competing water users which are given “penalty structures”The WRYM uses the hydrologic flows derived from the WRSM2000This study calculated water resources and water demands for quaternary catchments in all of South Africa, as well as Lesotho and Swaziland.2010 study on Assessment of the Ultimate Potential and Future Marginal Cost of Water Resources in South Africagives estimates on the future costs of securing water for all part of the country through interbasin transfers.